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Learner Reviews & Feedback for Gen AI Foundational Models for NLP & Language Understanding by IBM

4.4

stars

170 ratings

About the Course

This IBM course will equip you with the skills to implement, train, and evaluate generative AI models for natural language processing (NLP) using PyTorch. You will explore core NLP tasks, such as document classification, language modeling, and language translation, and gain a foundation in building small and large language models. You will learn how to convert words into features using one-hot encoding, bag-of-words, embeddings, and embedding bags, as well as how Word2Vec models represent semantic relationships in text. The course covers training and optimizing neural networks for document categorization, developing statistical and neural N-Gram models, and building sequence-to-sequence models using encoder–decoder architectures. You will also learn to evaluate generated text using metrics such as BLEU. The hands-on labs provide practical experience with tasks such as classifying documents using PyTorch, generating text with language models, and integrating pretrained embeddings like Word2Vec. You will also implement sequence-to-sequence models to perform tasks such as language translation. Enroll today to build in-demand NLP skills and start creating intelligent language applications with PyTorch....

Top reviews

Oct 26, 2025

The lab materials are very complicated could be made abstract using tensorflow.

Oct 13, 2025

Overall good course but the videos could use better pacing

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26 - 29 of 29 Reviews for Gen AI Foundational Models for NLP & Language Understanding

By Serghei I

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Jun 1, 2025

Challenging but insightful course. A solid grasp of discrete math is essential — otherwise, many concepts will be hard to follow. The course heavily relies on mathematical formulas, some of which deserve dedicated lessons. Certain coding labs use outdated libraries, which made reproducing results time-consuming due to API changes. Despite the fast pace and compressed explanations of complex topics, the course offers valuable foundational insights. Ideally, this material could be split into two or three separate courses for better depth and clarity.

By Muhammad A k

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Jan 3, 2025

The content is good but its very difficult to understand as its not for the beginners. Explanation part is very limited and labs are very detailed making it difficult to understand.

By Christian H

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Dec 20, 2025

I am a bit disappointed with this course. In a nutshell, if you know the content already and need a refresher then this is good. If this is the first time learning about the concepts... good luck. Lets start with the pros: 1) Labs are great 2) Overall the core course content is good Now to the cons: 1) The monotone AI narration is extremely annoying and often too fast 2) Some slides are just walking through code snippets quickly without proper explainations 3) I learned more from the labs then the course slides If this material is somewhat familiar, this is a good course. If you are here to learn something new, then be prepared to use other sources to understand the material. I am really struggling when I see courses about a fascinating topic that are getting dragged down by the implementation. Please use a human narrator who explains the content so that the learner does not have to use other sources. Also why can't we have longer videos? It feels that explanations are suffering because the content has to fit in an 6-8 min video. Don't underestimate your learners. They can handle a 20 min video, but they are not happy with a half-baked explanation.

By Ethan K

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Aug 27, 2025

This course is soooo boring. It feels like it's written by robots for robots. I want to see humans teaching material and making it understandable, interesting, and relatable. This is just ai-slop.